Recurrent constructor takes rho argument

1 files changed, 13 insertions, 7 deletions

diff --git a/test/test-all.lua b/test/test-all.lua
index a3999b8..ae2d17a 100644
--- a/test/test-all.lua
+++ b/test/test-all.lua
@@ -331,7 +331,7 @@ function nnxtest.Recurrent()
    local inputSize = 10
    local hiddenSize = 12
    local outputSize = 7
-   local nSteps = 5
+   local nSteps = 5 
    local inputModule = nn.Linear(inputSize, outputSize)
    local transferModule = nn.Sigmoid()
    -- test MLP feedback Module (because of Module:representations())
@@ -339,7 +339,8 @@ function nnxtest.Recurrent()
    feedbackModule:add(nn.Linear(outputSize, hiddenSize))
    feedbackModule:add(nn.Sigmoid())
    feedbackModule:add(nn.Linear(hiddenSize, outputSize))
-   local mlp = nn.Recurrent(outputSize, inputModule, feedbackModule, transferModule:clone())
+   -- rho = nSteps
+   local mlp = nn.Recurrent(outputSize, inputModule, feedbackModule, transferModule:clone(), nSteps)
    
    local gradOutputs, outputs = {}, {}
    -- inputs = {inputN, {inputN-1, {inputN-2, ...}}}}}
@@ -352,6 +353,8 @@ function nnxtest.Recurrent()
    mlp6:evaluate()
    
    mlp:zeroGradParameters()
+   local mlp7 = mlp:clone()
+   mlp7.rho = nSteps - 1
    for step=1,nSteps do
       local input = torch.randn(batchSize, inputSize)
       local gradOutput
@@ -368,6 +371,10 @@ function nnxtest.Recurrent()
       
       local output6 = mlp6:forward(input)
       mytester:assertTensorEq(output, output6, 0.000001, "evaluation error "..step)
+      
+      local output7 = mlp7:forward(input)
+      mlp7:backward(input, gradOutput)
+      mytester:assertTensorEq(output, output7, 0.000001, "rho = nSteps-1 forward error "..step)
 
       table.insert(gradOutputs, gradOutput)
       table.insert(outputs, output:clone())
@@ -380,15 +387,14 @@ function nnxtest.Recurrent()
    end
    local mlp4 = mlp:clone()
    local mlp5 = mlp:clone()
-   local mlp7 = mlp:clone() -- rho = nSteps - 1
    
    -- backward propagate through time (BPTT)
-   local gradInput = mlp:backwardThroughTime(nSteps) -- rho = nSteps
+   local gradInput = mlp:backwardThroughTime()
    mlp4.fastBackward = false
-   local gradInput4 = mlp4:backwardThroughTime(nSteps)
+   local gradInput4 = mlp4:backwardThroughTime()
    mytester:assertTensorEq(gradInput, gradInput4, 0.000001, 'error slow vs fast backwardThroughTime')
-   -- rho = nSteps - 1
-   mlp7:backwardThroughTime(nSteps-1) -- rho shouldn't update startModule
+   -- rho = nSteps - 1 : shouldn't update startModule
+   mlp7:backwardThroughTime()
    
    local mlp2 -- this one will simulate rho = nSteps
    local outputModules = {}